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The development of sensors for detection of biomarkers exhibits an exciting potential in diagnosis of diseases. Herein, we propose a novel electrochemical sensing strategy for label-free dual-biomarker detection, which is based on the combination of stimulus-responsive molecularly imprinted polymer (MIP)-modified nanopores and a polymeric membrane chronopotentiometric sensor. The ion fluxes galvanostatically imposed on the sensing membrane surface can be blocked by the recognition reaction between the target biomarker in the sample solution and the stimulus-responsive MIP receptor in the nanopores, thus causing a potential change. By using two external stimuli (i.e., pH and temperature), the recognition abilities of the stimulus-responsive MIP receptor can be effectively modulated so that dual-biomarker label-free chronopotentiometric detection can be achieved. Using alpha fetoprotein (AFP) and prostate-specific antigen (PSA) as model biomarkers, the proposed sensor offers detection limits of 0.17 and 0.42 ng/mL for AFP and PSA, respectively.

Magnetic nanoparticle (MNP)-based immunochromatographic tests (ICTs) display long-term stability and an enhanced capability for multiplex biomarker detection, surpassing conventional gold nanoparticles (AuNPs) and fluorescence-based ICTs. In this study, we innovatively developed zwitterionic silica-coated MNPs (MNP@Si-Zwit/COOH) with outstanding antifouling capabilities and effectively utilised them for the simultaneous identification of the nucleocapsid protein (N protein) of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) and influenza A/B. The carboxyl-functionalised MNPs with 10% zwitterionic ligands (MNP@Si-Zwit 10/COOH) exhibited a wide linear dynamic detection range and the most pronounced signal-to-noise ratio when used as probes in the ICT. The relative limit of detection (LOD) values were achieved in 12 min by using a magnetic assay reader (MAR), with values of 0.0062 ng/mL for SARS-CoV-2 and 0.0051 and 0.0147 ng/mL, respectively, for the N protein of influenza A and influenza B. By integrating computer vision and deep learning to enhance the image processing of immunoassay results for multiplex detection, a classification accuracy in the range of 0.9672–0.9936 was achieved for evaluating the three proteins at concentrations of 0, 0.1, 1, and 10 ng/mL. The proposed MNP-based ICT for the multiplex diagnosis of biomarkers holds substantial promise for applications in both medical institutions and self-administered diagnostic settings. [Display omitted] • Zwitterionic magnetic immunochromatographic assay systems were prepared. • Multiplex and diagnosis of biomarkers were performed under a magnetic reader. • Integration of computer vision and Deep-Learning were employed for imaging. [ABSTRACT FROM AUTHOR]